通过融合监督学习和强化学习的政策,让游戏玩法更接近人类

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tatsuyoshi Ogawa, Chu-Hsuan Hsueh, Kokolo Ikeda
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引用次数: 0

摘要

本文章由计算机程序翻译,如有差异,请以英文原文为准。
More Human-Like Gameplay by Blending Policies from Supervised and Reinforcement Learning
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来源期刊
IEEE Transactions on Games
IEEE Transactions on Games Engineering-Electrical and Electronic Engineering
CiteScore
4.60
自引率
8.70%
发文量
87
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